Training Neural Network with 'fake' data?
Yes, this is not advisable. If you train your model with random data your model is not learning anything useful, because there is no information to gain from those examples. Even worse it may (and likely is) trying to generalize off of your incorrect examples, which will lessen the effect your real examples have. Essentially, you are just dampening your training set with noise. 

You are moving in the right direction though. 75 examples will not be enough if your problem has any complexity at all. And unless, you know some correlation between the inputs a,b and the output c you don't want to generate data (and even if you did know some correlation, it is not always suggested to generate data). If it is impossible to get any more data you might want to consider a statistical model, rather than a neural network.